{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "89d93e45-8a95-4334-a93a-fa1d6c37e002",
   "metadata": {},
   "source": [
    "# <span style=\"color: #9333EA;\">七  百分比蝴蝶图</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb3a9fa1-10e9-4788-919b-1665d5d96953",
   "metadata": {},
   "source": [
    "## 将订单数改为订单总数的百分比，再次生成同样的蝴蝶图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83867ee4-6fe7-4cda-9413-3b35c4582336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各专营店订单数量统计:\n",
      "年份季度   2024Q4  2025Q1\n",
      "店铺名称                 \n",
      "专营店1        5      10\n",
      "专营店10       5      10\n",
      "专营店2        4       5\n",
      "专营店3        2       7\n",
      "专营店4        8       6\n",
      "专营店5        6       7\n",
      "专营店6        6      10\n",
      "专营店7        4       7\n",
      "专营店8        2       9\n",
      "专营店9        7       8\n",
      "\n",
      "2024年Q4季度总订单数: 49\n",
      "2025年Q1季度总订单数: 79\n",
      "\n",
      "各专营店订单百分比统计:\n",
      "       2024Q4_百分比  2025Q1_百分比\n",
      "专营店1        10.20       12.66\n",
      "专营店10       10.20       12.66\n",
      "专营店2         8.16        6.33\n",
      "专营店3         4.08        8.86\n",
      "专营店4        16.33        7.59\n",
      "专营店5        12.24        8.86\n",
      "专营店6        12.24       12.66\n",
      "专营店7         8.16        8.86\n",
      "专营店8         4.08       11.39\n",
      "专营店9        14.29       10.13\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "数据汇总:\n",
      "2024年Q4季度总订单数: 49\n",
      "2025年Q1季度总订单数: 79\n",
      "专营店数量: 10\n",
      "数据时间范围: 2024-10-20 23:35:57 到 2025-10-23 21:19:32\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "# 读取ERP订单数据\n",
    "data = pd.read_excel(\"ERP订单数据.xlsx\")\n",
    "\n",
    "# 1. 处理时间与季度\n",
    "# 根据您的数据结构，日期列可能是第5列（索引4），请根据实际情况调整列名\n",
    "date_column = data.columns[6]  # 调整为您数据中实际日期列的列名\n",
    "\n",
    "# 转换时间格式并处理错误\n",
    "data[\"下单时间\"] = pd.to_datetime(data[date_column], errors=\"coerce\")\n",
    "data = data.dropna(subset=[\"下单时间\"])\n",
    "\n",
    "# 提取年份和季度\n",
    "data[\"年份\"] = data[\"下单时间\"].dt.year\n",
    "data[\"季度\"] = data[\"下单时间\"].dt.quarter\n",
    "data[\"年份季度\"] = data[\"年份\"].astype(str) + \"Q\" + data[\"季度\"].astype(str)\n",
    "\n",
    "# 2. 定义目标季度范围\n",
    "target_quarters = [\"2024Q4\", \"2025Q1\"]\n",
    "\n",
    "# 3. 提取专营店数据（只包含\"专营店\"的行）\n",
    "zhuanying_data = data[data['店铺名称'].str.contains('专营店', na=False)]\n",
    "\n",
    "# 4. 筛选目标季度的数据\n",
    "target_data = zhuanying_data[zhuanying_data[\"年份季度\"].isin(target_quarters)]\n",
    "\n",
    "# 5. 按专营店和季度统计订单数量\n",
    "quarterly_counts = target_data.groupby(['店铺名称', '年份季度']).size().unstack(fill_value=0)\n",
    "\n",
    "# 确保两个季度都有列（即使某个季度没有数据）\n",
    "for quarter in target_quarters:\n",
    "    if quarter not in quarterly_counts.columns:\n",
    "        quarterly_counts[quarter] = 0\n",
    "\n",
    "# 按店铺名称排序\n",
    "quarterly_counts = quarterly_counts.sort_index()\n",
    "\n",
    "# 创建数据数组\n",
    "stores = quarterly_counts.index.tolist()\n",
    "q4_2024_counts = quarterly_counts.get(\"2024Q4\", pd.Series([0]*len(stores), index=stores)).values\n",
    "q1_2025_counts = quarterly_counts.get(\"2025Q1\", pd.Series([0]*len(stores), index=stores)).values\n",
    "\n",
    "# 计算各季度订单总数\n",
    "total_q4_2024 = sum(q4_2024_counts)\n",
    "total_q1_2025 = sum(q1_2025_counts)\n",
    "\n",
    "# 转换为百分比\n",
    "q4_2024_percent = (q4_2024_counts / total_q4_2024 * 100) if total_q4_2024 > 0 else np.zeros(len(stores))\n",
    "q1_2025_percent = (q1_2025_counts / total_q1_2025 * 100) if total_q1_2025 > 0 else np.zeros(len(stores))\n",
    "\n",
    "print(\"各专营店订单数量统计:\")\n",
    "print(quarterly_counts)\n",
    "print(f\"\\n2024年Q4季度总订单数: {total_q4_2024}\")\n",
    "print(f\"2025年Q1季度总订单数: {total_q1_2025}\")\n",
    "\n",
    "print(\"\\n各专营店订单百分比统计:\")\n",
    "percent_df = pd.DataFrame({\n",
    "    '2024Q4_百分比': q4_2024_percent,\n",
    "    '2025Q1_百分比': q1_2025_percent\n",
    "}, index=stores)\n",
    "print(percent_df.round(2))\n",
    "\n",
    "# 创建图形和轴\n",
    "fig, ax = plt.subplots(figsize=(14, 10))\n",
    "\n",
    "# 设置深蓝色背景\n",
    "fig.patch.set_facecolor('#1a1a3d')\n",
    "ax.set_facecolor('#1a1a3d')\n",
    "\n",
    "# 设置y轴位置\n",
    "y_pos = np.arange(len(stores))\n",
    "\n",
    "# 设置条形图宽度\n",
    "bar_width = 0.35\n",
    "\n",
    "# 创建蝴蝶图 - 2025Q1在左侧（负值），2024Q4在右侧（正值）\n",
    "# 左侧：2025年Q1季度百分比（红色）\n",
    "bars_left = ax.barh(y_pos, -q1_2025_percent, bar_width, \n",
    "                   label='2025年Q1季度', color='#e74c3c', alpha=0.8)\n",
    "\n",
    "# 右侧：2024年Q4季度百分比（蓝色）\n",
    "bars_right = ax.barh(y_pos, q4_2024_percent, bar_width, \n",
    "                    label='2024年Q4季度', color='#3498db', alpha=0.8)\n",
    "\n",
    "# 设置y轴在中间\n",
    "ax.spines['left'].set_position('center')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "\n",
    "# 设置y轴标签（店铺名称）\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(stores, color='white', fontsize=11)\n",
    "\n",
    "# 设置x轴标签和刻度\n",
    "ax.set_xlabel('订单百分比 (%)', color='white', fontsize=12, labelpad=15)\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "\n",
    "# 设置x轴刻度，显示绝对值百分比\n",
    "max_percent = max(max(q4_2024_percent), max(q1_2025_percent))\n",
    "x_ticks = np.arange(0, max_percent + 10, 10)  # 每10%一个刻度\n",
    "ax.set_xticks(np.concatenate([-x_ticks[1:][::-1], x_ticks]))\n",
    "ax.set_xticklabels([f'{int(x)}%' for x in np.concatenate([x_ticks[1:][::-1], x_ticks])])\n",
    "\n",
    "# 设置标题\n",
    "ax.set_title('各专营店2024年Q4季度 vs 2025年Q1季度订单百分比对比', \n",
    "             color='white', fontsize=16, pad=20, fontweight='bold')\n",
    "\n",
    "# 添加图例\n",
    "ax.legend(facecolor='#1a1a3d', edgecolor='white', labelcolor='white', \n",
    "          loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
    "\n",
    "# 在条形图上添加百分比标签\n",
    "for bar in bars_left:\n",
    "    width = -bar.get_width()  # 取绝对值\n",
    "    if width > 0.5:  # 只为大于0.5%的值添加标签，避免拥挤\n",
    "        ax.text(bar.get_width() - 1, bar.get_y() + bar.get_height()/2, \n",
    "                f'{width:.1f}%', \n",
    "                ha='right', va='center', color='white', fontsize=9, fontweight='bold')\n",
    "\n",
    "for bar in bars_right:\n",
    "    width = bar.get_width()\n",
    "    if width > 0.5:  # 只为大于0.5%的值添加标签，避免拥挤\n",
    "        ax.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2, \n",
    "                f'{width:.1f}%', \n",
    "                ha='left', va='center', color='white', fontsize=9, fontweight='bold')\n",
    "\n",
    "# 添加垂直网格线\n",
    "ax.grid(True, axis='x', alpha=0.2, color='white', linestyle='--')\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# 在中间添加垂直线\n",
    "ax.axvline(x=0, color='white', alpha=0.5, linewidth=1)\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(bottom=0.15)  # 为图例留出空间\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "\n",
    "# 可选：保存图表\n",
    "# plt.savefig('专营店订单百分比对比_蝴蝶图.png', dpi=300, facecolor='#1a1a3d', bbox_inches='tight')\n",
    "\n",
    "# 打印数据汇总\n",
    "print(f\"\\n数据汇总:\")\n",
    "print(f\"2024年Q4季度总订单数: {total_q4_2024}\")\n",
    "print(f\"2025年Q1季度总订单数: {total_q1_2025}\")\n",
    "print(f\"专营店数量: {len(stores)}\")\n",
    "print(f\"数据时间范围: {data['下单时间'].min()} 到 {data['下单时间'].max()}\")"
   ]
  },
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   "metadata": {},
   "outputs": [],
   "source": []
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